Brain computer interfaces (BCIs) function as a direct communication pathway between a human brain and an external device. Furthermore, BCI systems can also provide an important test-bed for the development of mathematical methods and multi-channel signal processing to derive command signals from brain activities. As it directly uses the electrical signatures of the brain's activity for responding to external stimuli, it is particularly useful for paralyzed people who suffer from severe neuromuscular disorders and are hence unable to communicate through the normal neuromuscular, pathway. The electroencephalogram (EEG) is one of the widely used techniques out of many existing brain signal measuring techniques due to its advantages such as its non-invasive nature and its low cost.
In addition to rehabilitation, BCI applications include, but are not limited to, communication, control, biofeedback and interactive computer gaming and entertainment computing.
For example, currently, stroke rehabilitation commonly involves physical therapy by human therapists. Alternatively, robotic rehabilitation may augment human therapists and enable novel rehabilitation exercises which may not be available from human therapists.
Typically, robotic rehabilitation includes rehabilitation based solely on movement repetition. In other words, a robot assists the patient even if the patient is not attentive towards therapy and robot assistance is triggered if no movement detected, for example, after a period of 2 seconds.
Moreover, robots deliver standardized rehabilitation, unlike human therapists who can deliver individualized rehabilitation based on the condition and progress of the stroke patient. Furthermore, the use of a robot may not be suitable for home-based rehabilitation where there are cost and space concerns. In addition, the main form of feedback to the patient is visual feedback provided through a screen, which may be insufficient.
Clinical trials involving brain-computer interface (BCI) based robotic rehabilitation are currently ongoing and some advantages over standard robotic rehabilitation include robotic assistance to the patient only if motor intent is detected and detection of motor intent being calibrated to patient-specific motor imagery electroencephalogram (EEG).
Embodiments of the present invention seek to improve on current brain-computer interface systems.